{
 "cells": [
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    }
   },
   "source": [
    "# Exercise 7) Learning and Planning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "grade_id": "cell-3353a9b5529d1970",
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   "source": [
    "In this exercise, we will again investigate the inverted pendulum from the `gymnasium` environment. We want to check, which benefits the implementation of planning offers.\n",
    "\n",
    "Please note that the parameter $n$ has a different meaning in the context of planning (number of planning steps per actual step) than in the context of n-step learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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     "grade": false,
     "grade_id": "cell-341da976c725d0ba",
     "locked": true,
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     "task": false
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from tqdm.notebook import tqdm\n",
    "import random\n",
    "import numpy as np\n",
    "import gymnasium as gym\n",
    "gym.logger.min_level=40\n",
    "plt.style.use('seaborn-v0_8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-92cdcff0f0a476ff",
     "locked": true,
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     "solution": false,
     "task": false
    }
   },
   "source": [
    "We will reuse the discretization routine from the previous exercise:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-be904d3f45f5ff7f",
     "locked": true,
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     "task": false
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   },
   "outputs": [],
   "source": [
    "d_T = 15\n",
    "d_theta = 15\n",
    "d_omega = 15\n",
    "\n",
    "\n",
    "def discretize_state(states):\n",
    "    limits = [1, 1, 8]\n",
    "    nb_disc_intervals = [d_theta, d_theta, d_omega]\n",
    "\n",
    "    # bring to value range [-1, 1]\n",
    "    norm_states = [state / limit for state, limit in zip(states, limits)]\n",
    "    interval_lengths = [2 / d for d in nb_disc_intervals]\n",
    "    disc_state = [(norm_state + 1) // interval_length for norm_state,\n",
    "                  interval_length in zip(norm_states, interval_lengths)]\n",
    "    disc_state = [(state - 1) if state == d else state for state,\n",
    "                  d in zip(disc_state, nb_disc_intervals)]  # ensure that disc_state < d\n",
    "\n",
    "    return np.array(disc_state, dtype=int)\n",
    "\n",
    "\n",
    "def continualize_action(disc_action):\n",
    "    limit = 2\n",
    "    interval_length = 2 / (d_T-1)\n",
    "    norm_action = disc_action * interval_length\n",
    "    cont_action = (norm_action - 1) * limit\n",
    "    return np.array(cont_action).flatten()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-4846b5fbbddf7cc5",
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    }
   },
   "source": [
    "## 1) Dyna-Q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "grade": false,
     "grade_id": "cell-785833d7145b35e8",
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   },
   "source": [
    "Write a Dyna-Q algorithm to solve the inverted pendulum. Check the quality of the result for different number of episodes, number of steps per episode and number of planning steps per interaction.\n",
    "\n",
    "Make sure that the total number of learning steps stays the same for different n, such that comparisons are fair:\n",
    "\n",
    "$\\text{episodes} \\cdot \\text{steps} \\cdot (1+n) = \\text{const.}$\n",
    "\n",
    "Interesting metrics for a comparison could be e.g. the execution time (the tqdm loading bar shows execution time of loops, alternatively you can use the time.time() command to get the momentary system time in seconds) and training stability. \n",
    "\n",
    "![](DynaQ_Algo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
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     "locked": true,
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     "task": false
    }
   },
   "source": [
    "## Solution 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "cell-bcde5eb71e92fdc2",
     "locked": false,
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     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "source": [
    "The solution code is given below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-8c85133baab1af6a",
     "locked": false,
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    }
   },
   "outputs": [],
   "source": [
    "def interact(env, pi, state, deterministic, epsilon):\n",
    "    \"\"\"Interact with the environment to get to the next state.\n",
    "\n",
    "    Args:\n",
    "        env: The environment to interact with\n",
    "        pi: The policy\n",
    "        state: The state before interaction\n",
    "        deterministic: Whether actions are chosen deterministically or eps-greedily\n",
    "        epsilon: The probability for random interaction\n",
    "\n",
    "    Returns:\n",
    "        next_state: The (discretized) next state after interaction\n",
    "        reward: The reward for the current interaction\n",
    "        terminated: If the goal was reached\n",
    "        action: The applied action\n",
    "    \"\"\"\n",
    "    ### BEGIN SOLUTION\n",
    "\n",
    "    if epsilon < np.random.uniform(0, 1) or deterministic:\n",
    "        action = pi[state].astype(int)\n",
    "    else:\n",
    "        action = np.random.choice(d_T) # explorative action\n",
    "\n",
    "    cont_action = continualize_action(action)\n",
    "    next_state, reward, terminated, _, _ = env.step(cont_action)\n",
    "    next_state = tuple(discretize_state(next_state).astype(int))\n",
    "\n",
    "    ### END SOLUTION\n",
    "    return next_state, reward, terminated, action\n",
    "\n",
    "\n",
    "def learn(action_values, pi, state, action, next_state, reward, gamma, alpha):\n",
    "    \"\"\"Learn from an interaction with the environment. This process is\n",
    "    identical for momentary data and data sampled from the model.\n",
    "\n",
    "    Args:\n",
    "        action_values: The action-value estimates before the update step\n",
    "        pi: The policy before the update step\n",
    "        state: The state before interaction\n",
    "        action: The chosen action\n",
    "        next_state: The state after interaction\n",
    "        reward: The reward for the interaction\n",
    "        gamma: Discount factor\n",
    "        alpha: Step size\n",
    "\n",
    "    Returns:\n",
    "        action_values: The updated action-value estimates\n",
    "        pi: The updated policy\n",
    "    \n",
    "    \"\"\"\n",
    "    ### BEGIN SOLUTION\n",
    "\n",
    "    action_values[state][action] += alpha * (\n",
    "        reward \n",
    "        + gamma * np.max(action_values[next_state])\n",
    "        - action_values[state][action]\n",
    "    )\n",
    "    pi[state] = np.argmax(action_values[state])\n",
    "\n",
    "    ### END SOLUTION\n",
    "    return action_values, pi\n",
    "\n",
    "\n",
    "def learn_planning_from_experience(action_values, pi, model, gamma, alpha):\n",
    "    \"\"\"Sample interactions from the environment and learn from them.\n",
    "\n",
    "    Args:\n",
    "        action_values: The action-value estimates before the update step\n",
    "        pi: The policy before the update step\n",
    "        model: The current model of past experiences\n",
    "        gamma: Discount factor\n",
    "        alpha: Step size\n",
    "\n",
    "    Returns:\n",
    "        action_values: The updated action-value estimates\n",
    "        pi: The updated policy\n",
    "\n",
    "    \"\"\"\n",
    "    ### BEGIN SOLUTION\n",
    "\n",
    "    # sample a random key from the dict:\n",
    "    # this state action combination has surely been taken in the past\n",
    "    state, action = random.sample(list(model.keys()), 1)[0]\n",
    "    next_state, reward = model[state, action]\n",
    "\n",
    "    action_values, pi = learn(\n",
    "        action_values, pi, state, action, next_state, reward, gamma, alpha\n",
    "    )\n",
    "\n",
    "    ### END SOLUTION   \n",
    "    return action_values, pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pendulumDynaQ(alpha, gamma, epsilon, n, nb_episodes, nb_steps):\n",
    "\n",
    "    env = gym.make('Pendulum-v1')\n",
    "    env = env.unwrapped\n",
    "\n",
    "    action_values = np.zeros([d_theta, d_theta, d_omega, d_T])\n",
    "    pi = np.zeros([d_theta, d_theta, d_omega])\n",
    "\n",
    "    model = {}\n",
    "\n",
    "    cumulative_reward_history = [] # we can use this to figure out how well the learning worked\n",
    "    pbar = tqdm(range(nb_episodes))\n",
    "\n",
    "    for _ in pbar:\n",
    "        \n",
    "        rewards = [] # this time, this list is only for monitoring\n",
    "\n",
    "        state, _ = env.reset() # initialize x_0\n",
    "        state = tuple(discretize_state(state).astype(int))\n",
    "\n",
    "        for _ in range(nb_steps):\n",
    "\n",
    "            ### BEGIN SOLUTION\n",
    "\n",
    "            # gather experience from the real environment\n",
    "            next_state, reward, terminated, action = interact(env, pi, state, False, epsilon)\n",
    "            model[state, action] = (next_state, reward) # save the latest experience in the model\n",
    "            \n",
    "            action_values, pi = learn(\n",
    "                action_values, pi, state, action, next_state, reward, gamma, alpha\n",
    "            )\n",
    "            \n",
    "            state = next_state\n",
    "            rewards.append(reward)     \n",
    "\n",
    "            for _ in range(n):\n",
    "                action_values, pi = learn_planning_from_experience(action_values, pi, model, gamma, alpha)\n",
    "\n",
    "            if terminated:\n",
    "                break\n",
    "\n",
    "            ### END SOLUTION\n",
    "        \n",
    "        pbar.set_description(f\"Cumulative_reward {np.sum(rewards):6.0f}, Progress\")\n",
    "        cumulative_reward_history.append(np.sum(rewards))\n",
    "\n",
    "    env.close()    \n",
    "    return cumulative_reward_history, pi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-ad72a11ce720c2ca",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "Function to evaluate and render the measurement using the gym environment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-1e3b5eabe632e0b0",
     "locked": true,
     "schema_version": 3,
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     "task": false
    }
   },
   "outputs": [],
   "source": [
    "def experiment(pi, nb_steps=300, render=False):\n",
    "# Runs the inverted pendulum experiment using policy pi for nb_steps steps\n",
    "\n",
    "    if render:\n",
    "        env = gym.make('Pendulum-v1', render_mode=\"human\")\n",
    "    else:\n",
    "        env = gym.make('Pendulum-v1')\n",
    "    env = env.unwrapped\n",
    "\n",
    "    state, _ = env.reset() # initialize x_0\n",
    "    state = tuple(discretize_state(state).astype(int))\n",
    "\n",
    "    for k in range(nb_steps):\n",
    "        if render:\n",
    "            env.render() # comment out for faster execution\n",
    "\n",
    "        next_state, reward, terminated, action = interact(\n",
    "            env, pi, state, deterministic=True, epsilon=0\n",
    "        )\n",
    "\n",
    "        state = next_state\n",
    "\n",
    "        if terminated:\n",
    "            break\n",
    "\n",
    "    env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "cell-17792193ff75f321",
     "locked": false,
     "points": 0,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "source": [
    "Let's use nb_episodes = 5000, nb_steps = 500, n = 0 as a first try. This is effectively Q learning.\n",
    "\n",
    "\\begin{align}\n",
    "5000 \\cdot 500 \\cdot (0+1) &= 2.5 \\cdot 10^6\n",
    "\\end{align}\n",
    "\n",
    "The resulting policy is satisfactory.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-6c921cba34a87b7e",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run without planning\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "174d939201a149f7b272d64eed1f20a5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train\n",
    "print(\"Run without planning\")\n",
    "no_planning_history, no_planning_pi = pendulumDynaQ(alpha=0.1, gamma=0.9, epsilon=0.1, n=0, nb_episodes=5000, nb_steps=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-6e95fa1942d32845",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# run and render the experiment\n",
    "experiment(no_planning_pi, nb_steps=300, render=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "cell-236faf8290c72bc3",
     "locked": false,
     "points": 0,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "source": [
    "Now let's try $n=9$ with the same nb_steps = 500:\n",
    "\n",
    "\\begin{align}\n",
    "\\text{nb\\_episodes} &= \\frac{2.5 \\cdot 10^6}{500 \\cdot (9+1)} = 500\n",
    "\\end{align}\n",
    "\n",
    "The resulting policy also looks good.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-e97227c315bad07d",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run with planning\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0dc749d3a17c45a49c5bff85c00a6fe9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train\n",
    "print(\"Run with planning\")\n",
    "with_planning_history, with_planning_pi = pendulumDynaQ(alpha=0.1, gamma=0.9, epsilon=0.1, n=9, nb_episodes=500, nb_steps=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-2950e36d13673fb9",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# run and render the experiment\n",
    "experiment(with_planning_pi, nb_steps=300, render=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-f874b3a97814be53",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "Now lets compare the cumulative rewards:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-4fc08c4ca8bbd031",
     "locked": true,
     "schema_version": 3,
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     "task": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(no_planning_history, label=\"no planning\")\n",
    "plt.plot(with_planning_history, label=\"planning from experience\")\n",
    "plt.xlabel(\"episode\")\n",
    "plt.ylabel(r\"$\\sum R$\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "cell-de1462d7fe9bdbed",
     "locked": false,
     "points": 0,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "source": [
    "\n",
    "The cumulative reward over the episodes both seems to have high variance.\n",
    "\n",
    "- Do you have an idea why? Consider the initial state of the environment after resetting.\n",
    "\n",
    "So why should we prefer the planning method?\n",
    "\n",
    "- **Planning leads to the agent interacting less often with the \"real\" environment, such that in the end less interaction time is needed.** To motivate this, imagine that you interact with a real test bench instead of a simulation and that every interaction with the test bench is costly either in terms of time or in terms of money. When compute costs are lower than interaction costs, it makes sense to revisit already gathered data instead of perfoming real interactions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-2810aacf498563db",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "## 2) Simulation-based planning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-eb709d007371a52d",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "Although it can be useful for small state spaces, building a system model by storing large amounts of state transitions like in task (1) is rarely feasible in engineering. As engineers, we are capable of a more efficient way of system modeling that we can utilize here: differential equations.\n",
    "\n",
    "Using a state-space model allows to efficiently integrate existing pre-knowledge into the Dyna-Q algorithm we already used. To do so, write a class `pendulum_model` that implements a model of the pendulum. This class should work similar to `gymnasium`: it should at least have a `step` and a `reset` method. In the step method, make use of forward Euler integration to simulate the system dynamics. In the reset method, allow to pass an optional initial state to the model, such that we can easily compare model and environment. If no initial state is passed to the `reset` function, a random initial state should be determined.\n",
    "\n",
    "Integrate this model into a Dyna-Q algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-4f43d13fe3da4001",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "Model of the pendulum in differential-equation form for change of the angular frequency $\\omega$ and the angle $\\theta$ depending on the torque $T_\\mathrm{u}$:\n",
    "\n",
    "\\begin{align*}\n",
    "\\dot{\\omega} &= -\\frac{3 g}{2 l} \\text{sin}(\\theta +\\pi) + \\frac{1}{J} T_\\mathrm{u}\n",
    "\\\\\n",
    "\\dot{\\theta} &= \\omega\n",
    "\\end{align*}\n",
    "\n",
    "\n",
    "\n",
    "Parameters (gravity constant $g$, mass $m$, length  $l$ and intertia $J$ of the pendulum):\n",
    "\n",
    "\\begin{align*}\n",
    "g&=10 \\, \\frac{\\text{m}}{\\text{s}^2}\n",
    "\\\\\n",
    "m&=1 \\, \\text{kg}\n",
    "\\\\\n",
    "l&=1 \\, \\text{m}\n",
    "\\\\\n",
    "J&=\\frac{1}{3} m l^2\n",
    "\\end{align*}\n",
    "\n",
    "Forward Euler integration:\n",
    "\n",
    "\\begin{align*}\n",
    "\\dot{x}(k T_S) \\approx \\frac{x[k+1] - x[k]}{T_S}\n",
    "\\end{align*}\n",
    "\n",
    "with sampling time $T_S = 0.05 \\, \\text{s}$\n",
    "\n",
    "Reward function:\n",
    "\n",
    "\\begin{align*}\n",
    "r_{k+1} = -(\\theta^2[k] + 0.1 \\, \\text{s}^2 \\cdot \\omega^2[k] + 0.001 \\frac{1}{(\\text{N}\\text{m})^2} \\cdot T_\\mathrm{u}^2[k])\n",
    "\\end{align*}\n",
    "\n",
    "Limitations of state and action space:\n",
    "\\begin{align*}\n",
    "\\theta &\\in [-\\pi, \\pi]\n",
    "\\\\\n",
    "\\omega &\\in [-8  \\, \\frac{1}{\\text{s}}, 8  \\, \\frac{1}{\\text{s}}]\n",
    "\\\\\n",
    "T_\\mathrm{u} &\\in [-2 \\, \\text{N}\\text{m}, 2 \\, \\text{N}\\text{m}]\n",
    "\\end{align*}\n",
    "\n",
    "And of course input and output space:\n",
    "\\begin{align*}\n",
    "\\text{action}&=T_\\mathrm{u}\n",
    "\\\\\n",
    "\\text{state}&=\n",
    "\\begin{bmatrix}\n",
    "\\text{cos}(\\theta)\\\\\n",
    "\\text{sin}(\\theta)\\\\\n",
    "\\omega\n",
    "\\end{bmatrix}\n",
    "\\end{align*}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-5b01fba780d2909f",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "## Solution 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "cell-5d0c5a3e7a69720d",
     "locked": false,
     "points": 0,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "source": [
    "Model-based planning does not necessarily run faster than experience-based planning. However, experience-based planning fails to cover the whole state space especially in the earlier episodes when there are too few experiences. On the other hand, model-based planning can, of course, only be performed if a state-space model with accurate parametrization is available. In order to overcome parametric deviations between state-space model and environment, one could even use the measurements from the actual environment in order to identify the parameters of the model during runtime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-11f8cac7e03b7f81",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "class pendulum_model:\n",
    "    def __init__(self, dt=0.05, m=1, g=10, l=1):\n",
    "        \"\"\"\n",
    "        Initializes the Pendulum Model.\n",
    "\n",
    "        Args:\n",
    "            dt (float): Sampling time in seconds.\n",
    "            m (float): Mass in kilograms.\n",
    "            g (float): Gravity in meters per second squared.\n",
    "            l (float): Length in meters.\n",
    "        \"\"\"\n",
    "\n",
    "        # BEGIN SOLUTION\n",
    "\n",
    "        self.max_speed = 8\n",
    "        self.max_torque = 2\n",
    "\n",
    "        self.dt = dt  # sampling time in s\n",
    "        self.g = g  # gravity in m / s^2\n",
    "        self.m = m  # mass in kg\n",
    "        self.l = l  # length in m\n",
    "\n",
    "        self.J = 1 / 3 * m * l ** 2  # pedulums moment of inertia in kg * m^2\n",
    "\n",
    "        # END SOLUTION\n",
    "\n",
    "    def reset(self, state=None):\n",
    "        \"\"\"\n",
    "        Resets the pendulum's state.\n",
    "\n",
    "        Args:\n",
    "            state (array): Initial state (theta, omega) in radians and radians per second.\n",
    "\n",
    "        Returns:\n",
    "            state (array): Current state after reset.\n",
    "        \"\"\"\n",
    "\n",
    "        # BEGIN SOLUTION\n",
    "\n",
    "        # if no state is give, set randomly\n",
    "        if np.any(state == None):\n",
    "            self.theta = np.random.uniform(-np.pi, +np.pi)\n",
    "            self.omega = np.random.uniform(-self.max_speed, +self.max_speed)\n",
    "\n",
    "        # else set initial state as given\n",
    "        else:\n",
    "            self.theta = np.arctan2(state[1], state[0])\n",
    "            self.omega = state[2]\n",
    "\n",
    "        state = np.array([np.cos(self.theta), np.sin(self.theta), self.omega])\n",
    "\n",
    "        # END SOLUTION\n",
    "\n",
    "        return state\n",
    "\n",
    "    def step(self, T_u):\n",
    "        \"\"\"\n",
    "        Takes a step in the simulation.\n",
    "\n",
    "        Args:\n",
    "            T_u (float): Control torque applied.\n",
    "\n",
    "        Returns:\n",
    "            state (array): Current state after the step.\n",
    "            reward (float): Reward associated with the step.\n",
    "        \"\"\"\n",
    "\n",
    "        # BEGIN SOLUTION\n",
    "\n",
    "        T_u = np.clip(T_u, -self.max_torque, +self.max_torque)[0]\n",
    "\n",
    "        reward = -(self.angle_normalize(self.theta) ** 2 +\n",
    "                   0.1 * self.omega ** 2 + 0.001 * (T_u ** 2))\n",
    "\n",
    "        # differential-equations for state values\n",
    "        self.omega = self.omega + self.dt * \\\n",
    "            (-3 * self.g/(2 * self.l) * np.sin(self.theta + np.pi) + 1 / self.J * T_u)\n",
    "        self.theta = self.theta + self.dt * self.omega\n",
    "\n",
    "        self.omega = np.clip(self.omega, -self.max_speed, +self.max_speed)\n",
    "\n",
    "        state = np.array([np.cos(self.theta), np.sin(self.theta), self.omega])\n",
    "\n",
    "        # END SOLUTION\n",
    "\n",
    "        return state, reward\n",
    "\n",
    "    def angle_normalize(self, theta):\n",
    "        \"\"\"\n",
    "        Normalizes the angle.\n",
    "\n",
    "        Args:\n",
    "            theta (float): Angle in radians.\n",
    "\n",
    "        Returns:\n",
    "            float: Normalized angle in radians.\n",
    "        \"\"\"\n",
    "\n",
    "        # usage of this helper function is optional\n",
    "        return (((theta+np.pi) % (2*np.pi)) - np.pi)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-21f127639f04da34",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "The following cell is for debugging of the `pendulum_model` class. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-1c1d572fbaded77d",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "env = gym.make('Pendulum-v1')\n",
    "# removes a builtin time limit of k_T = 200, we want to set the time limit ourselves\n",
    "env = env.unwrapped\n",
    "\n",
    "model = pendulum_model()\n",
    "\n",
    "state, _ = env.reset()\n",
    "\n",
    "m_state = model.reset(state)  # model is set to state of env\n",
    "\n",
    "nb_episodes = 10000\n",
    "\n",
    "for _ in range(nb_episodes):\n",
    "\n",
    "    action = env.action_space.sample()\n",
    "\n",
    "    state, reward, terminated, _, _ = env.step(action)  # take action on env\n",
    "    m_state, m_reward = model.step(action)  # take the same action on model\n",
    "\n",
    "env.close()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-48d9727d1bcf9cba",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "Using $-\\mathrm{sin}(\\theta)$ instead of $\\mathrm{sin}(\\theta +\\pi)$ makes no difference when assuming analytical precision, but due to numeric errors these formulations will still yield different results in numpy, mainly because $\\pi$ is represented with finite (float) precision. In order to yield the same numbers as in `gymnasium`, we will still make use of the (more cumbersome) $\\mathrm{sin}(\\theta +\\pi)$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-0a63f527a46b3115",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "Write a function for the Dyna-Q algorithm, which uses the model we defined above: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You may reuse interact and learn from the previous task\n",
    "\n",
    "def learn_planning_from_model(action_values, pi, model, gamma, alpha):\n",
    "    \"\"\"Sample interactions from the environment and learn from them.\n",
    "\n",
    "    Args:\n",
    "        action_values: The action-value estimates before the update step\n",
    "        pi: The policy before the update step\n",
    "        model: The current model of the environment\n",
    "        gamma: Discount factor\n",
    "        alpha: Step size\n",
    "\n",
    "    Returns:\n",
    "        action_values: The updated action-value estimates\n",
    "        pi: The updated policy\n",
    "\n",
    "    \"\"\"\n",
    "    ### BEGIN SOLUTION\n",
    "                \n",
    "    state = model.reset()  # if no state is passed to the model, state is initialized randomly\n",
    "    action = np.random.choice(d_T)\n",
    "\n",
    "    state = tuple(discretize_state(state).astype(int))\n",
    "    cont_action = continualize_action(action)\n",
    "\n",
    "    next_state, reward = model.step(cont_action)\n",
    "    next_state = tuple(discretize_state(next_state).astype(int))\n",
    "\n",
    "    action_values, pi = learn(\n",
    "        action_values, pi, state, action, next_state, reward, gamma, alpha\n",
    "    )\n",
    "\n",
    "    ### END SOLUTION\n",
    "    return action_values, pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-ab054c945a614362",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "def pendulumModelDynaQ(alpha, gamma, epsilon, n, nb_episodes, nb_steps, m=1, g=10, l=1):\n",
    "\n",
    "    env = gym.make('Pendulum-v1')  # , render_mode=\"human\"\n",
    "    env = env.unwrapped\n",
    "    model = pendulum_model(m=m, g=g, l=l)\n",
    "\n",
    "    action_values = np.zeros([d_theta, d_theta, d_omega, d_T])\n",
    "    pi = np.zeros([d_theta, d_theta, d_omega])\n",
    "\n",
    "    # we can use this to figure out how well the learning worked\n",
    "    cumulative_reward_history = []\n",
    "\n",
    "    pbar = tqdm(range(nb_episodes), position=0, leave=True)\n",
    "\n",
    "    for _ in pbar:\n",
    "\n",
    "        ### BEGIN SOLUTION\n",
    "\n",
    "        rewards = []  # this time, this list is only for monitoring\n",
    "\n",
    "        state, _ = env.reset()  # initialize x_0\n",
    "        state = tuple(discretize_state(state).astype(int))\n",
    "\n",
    "        for k in range(nb_steps):\n",
    "\n",
    "            next_state, reward, terminated, action = interact(\n",
    "                env, pi, state, False, epsilon\n",
    "            )\n",
    "            # no model update is needed\n",
    "\n",
    "            action_values, pi = learn(\n",
    "                action_values, pi, state, action, next_state, reward, gamma, alpha\n",
    "            )\n",
    "            \n",
    "            state = next_state\n",
    "            rewards.append(reward)\n",
    "\n",
    "            for i in range(n):\n",
    "                action_values, pi = learn_planning_from_model(action_values, pi, model, gamma, alpha)\n",
    "\n",
    "            if terminated:\n",
    "                break\n",
    "\n",
    "            ### END SOLUTION\n",
    "\n",
    "        pbar.set_description(\n",
    "            f\"Cumulative_reward {np.sum(rewards):6.0f}, Progress\")\n",
    "\n",
    "        cumulative_reward_history.append(np.sum(rewards))\n",
    "\n",
    "    env.close()\n",
    "\n",
    "    # END SOLUTION\n",
    "\n",
    "    return cumulative_reward_history, pi\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-6f4a8238fb568bf1",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "Use the following cell to compare the learing from experience from 1) to the learning using the defined model: (Beware, nb_steps = 10000 can take some time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-55a20ee55df4b168",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run with planning from experience\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5ea498ff8e5c441886ef8f9e00e98ac4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train both setups once\n",
    "print(\"Run with planning from experience\")\n",
    "exp_planning_history, exp_planning_pi = pendulumDynaQ(alpha=0.1, gamma=0.9, epsilon=0.1, n=19, nb_episodes=500, nb_steps=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run with planning from model\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "db4414c5f70743d2af7814620d59dabe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Run with planning from model\")\n",
    "model_planning_history, model_planning_pi = pendulumModelDynaQ(alpha=0.1, gamma=0.9, epsilon=0.1, n=19, nb_episodes=500, nb_steps=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(exp_planning_history, label=\"planning from experience\")\n",
    "plt.plot(model_planning_history, label=\"planning from model\")\n",
    "plt.xlabel(\"episode\")\n",
    "plt.ylabel(r\"$\\sum R$\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-1a7bfac51c16c83f",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "Use the following cell to execute the policy we got using the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-05c7abf525fbaa40",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "experiment(model_planning_pi, nb_steps=300, render=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-54c9b97a5d245359",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "Now we change the model parameters (e.g. $g$, $m$, $l$) so that our model differs from the \"real world\" (the gym environment).\n",
    "What do you observe? You can also change the parameters to other values yourself.\n",
    "\n",
    "The Following learning curve results from a parameter change using $g =20 \\, \\frac{\\text{m}}{\\text{s}^2}, m = 5 \\, \\text{kg and } l = 2 \\, \\text{m}$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "cell-a6e85462116fce6c",
     "locked": false,
     "points": 0,
     "schema_version": 3,
     "solution": true,
     "task": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3fa176be5bbe4c9ca6368af2331f0194",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wrong_model_history, wrong_model_pi = pendulumModelDynaQ(\n",
    "    alpha=0.1, gamma=0.9, epsilon=0.1, n=19, nb_episodes=500, nb_steps=500, m=5, g=20, l=2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "cell-27a52b7e54657710",
     "locked": false,
     "points": 0,
     "schema_version": 3,
     "solution": true,
     "task": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(exp_planning_history, label=\"planning from experience\")\n",
    "plt.plot(wrong_model_history, label=\"planning from poor model\")\n",
    "plt.xlabel(\"episode\")\n",
    "plt.ylabel(r\"$\\sum R$\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "cell-2bc64d2fdf9455ef",
     "locked": false,
     "points": 0,
     "schema_version": 3,
     "solution": true,
     "task": false
    },
    "tags": []
   },
   "source": [
    "By changing the parameters our model differs from the \"real world\". Depending on the amount of difference, the learing curve looks worse than the one with the correct values. \n",
    "The experiment result is also depending on the random starting position. \n",
    "Depending on the parameter difference, the experiment can not be executed successfully any more. \n",
    "\n",
    "**In conclusion**: A good model can outperform experience gathered from the environment, but a poor model choice can also hinder the training process."
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Create Assignment",
  "kernelspec": {
   "display_name": "RL_course_py12",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
